Final Project

knitr::include_graphics("IMG_0667.JPG")

# Set chunk options to hide warnings, messages, and errors
knitr::opts_chunk$set(
  warning = FALSE,
  message = FALSE,
  error = FALSE,   # <- This hides error messages too
  include = TRUE
)

library(tidyverse)
library(dplyr)
library(stringr)
library(easystats)
library(equatiomatic)
library(pander)
library(broom)
library(kableExtra)
library(rmdformats)
library(prettydoc)
library(hrbrthemes)
library(tint)
library(tufte)
library(flexdashboard)



dat <- read.csv("plate_reading_2.csv")

1 Introduction

We will look at an MTT assay performed on COS-7 cells, which are cancerous monkey kidney cells, using different sterilization reagents. The chemicals used were water, ethanol, and hydrogen peroxide. Water was used to ensure that it wasn’t only the diluting of media that was causing cell death. Diluting media with water causes cells to not have as much nutrients so there is some expected cell death. Ethanol and Hydrogen peroxide are often used in the lab setting as sterilizing reagents, so we were curious to see what the effect on eukaryotic cells are. The standard which are the two first wells are to create a standard curve of “cell death” to compare the results to because the cell numbers within the ethanol and hydrogen peroxide all had 12,000 cells to see what the affect of the reagents were. So let’s take a look at the data!

print(dat)

##   Well standard Cell_count standard.1 Cell_count.1 water Cell_count.2
## 1    A    0.560      12000      0.475        12000 0.204        12000
## 2    B    0.382      10000      0.404        10000 0.203        10000
## 3    C    0.454       8000      0.422         8000 0.283         8000
## 4    D    0.350       6000      0.326         6000 0.406         6000
## 5    E    0.153       4000      0.200         4000 0.679         4000
## 6    F    0.160       2000      0.155         2000 0.766         2000
## 7    G    0.141       1000      0.130         1000 0.681         1000
## 8    H    0.111        500      0.108          500 0.870          500
##   Concentration water.1 Cell_count.3 Concentration.1 ethanol Cell_count.4
## 1        0.5000   0.138        12000          0.5000   0.102        12000
## 2        0.2500   0.154        10000          0.2500   0.104        12000
## 3        0.1250   0.207         8000          0.1250   0.105        12000
## 4        0.0625   0.219         6000          0.0625   0.119        12000
## 5        0.0312   0.404         4000          0.0312   0.263        12000
## 6        0.0156   0.572         2000          0.0156   0.494        12000
## 7        0.0078   0.694         1000          0.0078   0.590        12000
## 8        0.0039   0.803          500          0.0039   0.727        12000
##   Concentration.2 ethanol.1 Cell_count.5 Concentration.3 hydrogen_peroxide
## 1          0.5000     0.108        12000          0.5000             0.106
## 2          0.2500     0.107        12000          0.2500             0.100
## 3          0.1250     0.103        12000          0.1250             0.103
## 4          0.0625     0.125        12000          0.0625             0.125
## 5          0.0312     0.158        12000          0.0312             0.158
## 6          0.0156     0.187        12000          0.0156             0.187
## 7          0.0078     0.312        12000          0.0078             0.312
## 8          0.0039     0.366        12000          0.0039             0.336
##   Cell_count.6 Concentration.4 hydrogen_peroxide.1 Cell_count.7 Concentration.5
## 1        12000          0.0750               0.112        12000          0.0750
## 2        12000          0.0370               0.101        12000          0.0370
## 3        12000          0.0180               0.100        12000          0.0180
## 4        12000          0.0090               0.094        12000          0.0090
## 5        12000          0.0040               0.092        12000          0.0040
## 6        12000          0.0020               0.098        12000          0.0020
## 7        12000          0.0010               0.095        12000          0.0010
## 8        12000          0.0005               0.085        12000          0.0005

Looking at this data it is a little messy so we need to clean it up in order to properly perform any type of analysis on it! So what are the steps of cleaning data, well here they are!!

Cleaning data steps:

Import
Clean
Visualize
Model
Report

We have already imported our data, so let’s go ahead and clean it!

2 Cleaning data

library(tidyverse)
library(broom)
library(dplyr)
library(bslib)
library(shiny)

dat <- read.csv("plate_reading_2.csv")
names(dat)

##  [1] "Well"                "standard"            "Cell_count"         
##  [4] "standard.1"          "Cell_count.1"        "water"              
##  [7] "Cell_count.2"        "Concentration"       "water.1"            
## [10] "Cell_count.3"        "Concentration.1"     "ethanol"            
## [13] "Cell_count.4"        "Concentration.2"     "ethanol.1"          
## [16] "Cell_count.5"        "Concentration.3"     "hydrogen_peroxide"  
## [19] "Cell_count.6"        "Concentration.4"     "hydrogen_peroxide.1"
## [22] "Cell_count.7"        "Concentration.5"

dat_1 <- dat %>%
  pivot_longer(cols = starts_with("cell_count"),
               names_to = "measurement",
               values_to = "cell_count")

dat_1 <- dat %>% 
  rename_with(~ sub("//.//d+$", "", .x)) %>%
  pivot_longer(cols = c("Cell_count", "Cell_count.1", "Cell_count.2", 
                        "Cell_count.3", "Cell_count.4", "Cell_count.5", 
                        "Cell_count.6", "Cell_count.7"), 
               names_to = "Measurement", 
               values_to = "cell_count")

dat_2 <- dat_1 %>% 
  rename_with(~ sub("//.//d+$", "", .x)) %>%
  pivot_longer(cols = c("standard", "standard.1", "water", "water.1", 
                        "ethanol", "ethanol.1", "hydrogen_peroxide", 
                        "hydrogen_peroxide.1"), 
               names_to = "reagent", 
               values_to = "absorbance")

dat_3 <- dat_2 %>% 
  pivot_longer(cols = c("Concentration", "Concentration.1", 
                        "Concentration.2", "Concentration.3", 
                        "Concentration.4", "Concentration.5"), 
               names_to = "concentration.1", 
               values_to = "concentration")

dat_cleaned <- dat_3 %>% 
  select("Well", 
         "cell_count", 
         "reagent", 
         "absorbance", 
         "concentration")

Now let us look at the data in a table and put them into models.

3 Clean Data and Modeling

library(shinyjs)

ui <- fluidPage(
  useShinyjs(),
  theme = bs_theme(bootswatch = "minty"),
  titlePanel("Final Project: Cell Death Data"),
  
  tabsetPanel(
    tabPanel("Collapsible Table",
      actionButton("toggle_table", "Show/Hide Table"),
      uiOutput("table_output")
    ),
    
    tabPanel("Model Dashboard",
      sidebarLayout(
        sidebarPanel(
          selectInput("model_choice", "Choose a Model:",
                      choices = c("Model 1: concentration ~ absorbance" = "mod_1",
                                  "Model 2: concentration ~ concentration * absorbance" = "mod_2",
                                  "Model 3: cell_count ~ absorbance" = "mod_3"),
                      selected = "mod_1"),
          selectInput("reagent_filter", "Filter by Reagent:",
                      choices = c("All", "Water", "Ethanol", "Hydrogen Peroxide"),
                      selected = "All"),
          checkboxInput("log_trendline", "Add Logarithmic Trendline", value = FALSE)
        ),
        
        mainPanel(
          tabsetPanel(
            tabPanel("Model Summary", verbatimTextOutput("model_summary")),
            tabPanel("Actual vs Predicted Plot", plotOutput("prediction_plot")),
            tabPanel("Model Metrics", verbatimTextOutput("model_metrics"))
          )
        )
      )
    )
  )
)

server <- function(input, output, session) {
  
  # Collapsible Table
  output$table_output <- renderUI({
    req(input$toggle_table)
    if (input$toggle_table %% 2 == 1) {
      DT::dataTableOutput("table")
    }
  })
  
  output$table <- DT::renderDataTable({
    dat_cleaned
  })
  
  # Models
  mod_1 <- glm(concentration ~ absorbance, data = dat_cleaned)
  mod_2 <- glm(concentration ~ concentration * absorbance, data = dat_cleaned)
  mod_3 <- glm(cell_count ~ absorbance, data = dat_cleaned)
  
  model_lookup <- list(mod_1 = mod_1, mod_2 = mod_2, mod_3 = mod_3)
  
  selected_model <- reactive({
    model_lookup[[input$model_choice]]
  })
  
  output$model_summary <- renderPrint({
    summary(selected_model())
  })
  
  output$prediction_plot <- renderPlot({
    model <- selected_model()
    preds <- predict(model)
    actual <- model$y
    
    ggplot(data.frame(actual = actual, predicted = preds),
           aes(x = actual, y = predicted)) +
      geom_point(alpha = 0.6, color = "#2c3e50") +
      geom_abline(slope = 1, intercept = 0, linetype = "dashed", color = "red") +
      labs(title = "Actual vs Predicted", x = "Actual", y = "Predicted") +
      theme_minimal()
  })
  
  output$model_metrics <- renderPrint({
    model <- selected_model()
    preds <- predict(model)
    actual <- model$y
    rmse <- sqrt(mean((actual - preds)^2))
    r2 <- cor(actual, preds)^2
    
    cat("RMSE:", round(rmse, 3), "\n")
    cat("R²:", round(r2, 3), "\n")
  })
}

shinyApp(ui = ui, server = server)

Shiny applications not supported in static R Markdown documents

After looking at these models let us compare them

4 Visualize

dat_cleaned %>% 
  ggplot(aes(x = concentration, 
             y = absorbance, 
             color = reagent)) + 
  geom_point() +
  facet_wrap(~reagent)

Here is a visible picture of the results

5 Conclusion

Looking at the image, there is a nice gradient of purple where the less of a reagent was used, the darker it was, which means that there are more cells present here because the mitochondria is converting formazan crystals to get that purple color. So as a visible cue the reagents kill off these cells. Looking at these graphs we can see that hydrogen peroxide will kill anything it touches. The surprising thing is that ethanol and water follow the same trend of cell amounts. This could be because the ethanol wasn’t concentrated enough, but we can contribute some of the cell death to the DMSO. The modeling dashboard provides insight into how absorbance relates to both concentration and cell count, with model comparisons guiding which has better predictive power.

Possible points of error could have been as we learned with our previous run to use parafilm to ensure that the ethanol isn’t going to evaporate off. Understanding that hydrogen peroxide is so strong, even at such low concentrations it killed off most cells so figuring out what we could do for another run. If we were to run this experiment again, we would dilute the hydrogen peroxide even more to get it so dilute to where cells are able to survive. Rerunning this experiment would be a great idea for the purpose of reproducing results to ensure that we just didn’t get a one time number rather our results would become more reliable. Maybe adding another reagent, or running with different reagents over all. Seeing the effects of the caffeine for example on kidney cells or how some vitamins affect kidney cells. Maybe using a different cell line to see what the difference is in monkey kidney cells versus say a rabbit or a rat’s kidney cells.